The term “time-series” is used herein to refer to data representing a collection of observations made sequentially through time. Time-series forecasting is a method of computing forecasts based on present and past values of a time-series. The complexity of this process can range from something simple (like an average of the past two historic values) to more advanced mathematical concepts.
Time-series forecasts can be provided for many physical systems, particularly complex non-linear systems whose behaviour is difficult to predict using other methods. For example, time-series data may be collected from test data and used to predict future behaviour, for example, in the field of material science to determine when a particular part of a device when be subject to failure (e.g. from metal fatigue).
Whilst future behaviour may be mathematically modelled for simple systems and structures, for more complicated devices the use of time-series test data together with an appropriate forecasting engine can produce more accurate results and can produce results more rapidly than other methods. Accurate prediction of device failure is particularly important in industries such as the aeronautical and automobile industry, where device failure can have catastrophic consequences. A time-series forecast system may have to generate results rapidly as the data set on which the system works evolves, and the system itself may require modification to take into account changes in the historic data set over time. Such dynamic forecasting systems need to be designed in such a way that they can rapidly adapt to the characteristics of the data set for which they are generating results.
Time-series forecasting is also used in many other fields, ranging from weather prediction to predictions of system performance for complex systems such as might occur in agriculture or astronomy. Financial and business focussed time-series data are also collected for analysis purposes. Examples of such time-series include data representing sales of a particular product in successive months, and data representing work demand volumes over a number of days.
Time-series forecasting is becoming increasingly important for a variety of industries, enabling important resources such as component and product stores as well as the workforce of a company to be managed more efficiently. Such systems are frequently very complex and cannot be effectively modelled using manual techniques if accurate results are to be obtained in a given amount of time.
The wide variety of potential scenarios for which time-series forecasting is required has resulted in a demand for time-series forecast applications to be customised for specific requirements. More information on the general field of time-series forecasting can be found in “Time-Series Forecasting”, by C. Chatfield, pp 1-2, Chapman & Hall, 2000, ISBN 1-58488-063-5.
To predict future behaviour in any system, data is first collected in a “raw” or unprocessed form. Whilst simple forecast systems may generate forecasts based on the raw data collected, more sophisticated forecasting engines can receive data from a number of sources, and may need to “groom” the data to a standard form (for example, by normalising the data) prior to the data being processed by the forecasting system's algorithm(s). Further levels of sophistication can be incorporated by aggregating/de-aggregating the raw data etc, and in the choice and number of parameter values forming the time series.
Analysis of any complex system (whether physical or not) by a forecast system designer may require the designer to construct a suitable problem space comprising several parameters, which may have quite complex interrelationships, and time-series data is then acquired comprising a series of values for one or more selected parameters. A suitable forecasting system must then be constructed to determine characteristics of the data set which influence its future behaviour, i.e., to predict the value or values of one or more of the parameters forming the historic data set on which the forecasting system operates.
Customising a time-series forecasting system so that it is able to provide forecasts for specific requirements is a complex, costly task which can involve considerable reconfiguration of the forecasting system to determine which forecast algorithm(s) generate the most accurate results. A forecast system designer needs to reconfigure conventional forecasting systems each time additional parameters are introduced or deleted from the time-series data used by the forecast model of the forecast system.
As an example, consider the case where a forecast is required in order to ensure appropriate human resources are available for a company to fulfil its likely work requirements. In order to produce accurate forecasts, any model of the system must represent the critical features of the system. This results in a complex model comprising numerous parameters, such as geographic area, type of work etc. In such complex systems, the forecast designer must often proceed to some extent by trial and error to determine what parameters are critical to the system and to then determine what their relationship and relative weights are etc. to construct the rule set for the forecasting heuristic, which provides logic for coordinating the use of any forecast algorithms. In many scenarios, a large number of potential forecasting parameters exist which cause problems as it may not be clear when the forecasting tool is being developed which parameters (e.g., location, type of task etc.) are required to ensure the forecast is satisfactorily accurate.
U.S. patent application Ser. No. US2002/0133385A1, entitled “Method and computer program product for weather adapter consumer event planning”, by F. Fox, D. Pearson et al., describes a system for forecasting future retail performance in which a basic architecture consisting of an analyser and a configurator selects the specific parameters to be forecast over. However, if the parameters used in the model change, e.g., if instead of examining historical data comprising a time-series associated with a particular type of location for job (a location parameter), the data comprises time-series data relating to the time it takes to perform a job (a duration parameter), or if a location parameter “town” were changed to a location parameter for “street”, the configurator itself will have to be modified accordingly, in addition to the required database changes.
Similarly, in U.S. patent application Ser. No. US 2002/0169657A1 entitled “Supply chain demand system and forecasting”, by N. Singh, S. Olasky et all., a forecasting system is described which supports multi-scenario comparisons. Singh et al define how multiple algorithms may be compared and not how they are defined, their system uses different algorithms for different scenarios and does not deal with parameters in a generic and extensible way.
The invention seeks to obviate or mitigate one or more limitations and problems known in the art associated with the development of a forecasting system.
This invention seeks to facilitate the task of generating a forecasting system by providing a forecasting tool for developers of forecasting systems.